{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "-Zrph_W-uYmA",
    "outputId": "2ba7d839-d97b-4371-97f1-00041a0c059f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L2axUqJyugan"
   },
   "outputs": [],
   "source": [
    "# inspired by : \n",
    "# https://github.com/tensorflow/examples/blob/29f3cb71b31f32737907536a3ee9223a6970e68c/lite/examples/speech_commands/ml/download.py\n",
    "# tutorial at: https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/sequences/audio_recognition.md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 190
    },
    "colab_type": "code",
    "id": "R3_2FESVuteE",
    "outputId": "7ffcfbb5-bb3b-430c-f504-403ae9a1bb8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting wget\n",
      "  Downloading https://files.pythonhosted.org/packages/47/6a/62e288da7bcda82b935ff0c6cfe542970f04e29c756b0e147251b2fb251f/wget-3.2.zip\n",
      "Building wheels for collected packages: wget\n",
      "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for wget: filename=wget-3.2-cp36-none-any.whl size=9682 sha256=643f26d0f6faf4a86b0cfd452913eb2e7475ba56c976843c45463e9b7420f3c7\n",
      "  Stored in directory: /root/.cache/pip/wheels/40/15/30/7d8f7cea2902b4db79e3fea550d7d7b85ecb27ef992b618f3f\n",
      "Successfully built wget\n",
      "Installing collected packages: wget\n",
      "Successfully installed wget-3.2\n"
     ]
    }
   ],
   "source": [
    "!pip install wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1QMOu5xeuhsd"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import wget\n",
    "import tarfile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 164
    },
    "colab_type": "code",
    "id": "njRCFwcavbwy",
    "outputId": "6e061929-cc41-47f9-9885-d9488fb2b167"
   },
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-e1cd51241429>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/gdrive/My Drive/sound_commands'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mFileExistsError\u001b[0m: [Errno 17] File exists: '/gdrive/My Drive/sound_commands'"
     ]
    }
   ],
   "source": [
    "os.mkdir('/gdrive/My Drive/sound_commands')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0zNON_8fusku"
   },
   "outputs": [],
   "source": [
    "os.chdir('/gdrive/My Drive/sound_commands')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j1y-18b0vPlZ"
   },
   "outputs": [],
   "source": [
    "DATASET_URL = 'http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "qarXHTJ5vJV-",
    "outputId": "854664cf-7d1e-40c0-9e27-ea6a1452fe37"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'speech_commands_v0.01.tar.gz'"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wget.download(DATASET_URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ewn__cSpvS5a"
   },
   "outputs": [],
   "source": [
    "ARCHIVE = os.path.basename(DATASET_URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XtWNna1Cv7kA"
   },
   "outputs": [],
   "source": [
    "with tarfile.open(ARCHIVE, 'r:gz') as tar:\n",
    "  tar.extractall(path='data/train')\n",
    "\n",
    "os.remove(ARCHIVE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "SdU6DPCov9N-",
    "outputId": "ac2c8200-8c09-4580-e6e5-12c8a851bbcc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_background_noise_  five     marvin\tright\t\t  tree\n",
      "bed\t\t    four     nine\tseven\t\t  two\n",
      "bird\t\t    go\t     no\t\tsheila\t\t  up\n",
      "cat\t\t    happy    off\tsix\t\t  validation_list.txt\n",
      "dog\t\t    house    on\t\tstop\t\t  wow\n",
      "down\t\t    left     one\ttesting_list.txt  yes\n",
      "eight\t\t    LICENSE  README.md\tthree\t\t  zero\n"
     ]
    }
   ],
   "source": [
    "!ls data/train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yMwBGtp7Z7rG"
   },
   "source": [
    "let's take bed, bird, and tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    },
    "colab_type": "code",
    "id": "R3ASosTcbfmU",
    "outputId": "5c0feb83-bdd2-4f17-8f38-21cadae16f53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: librosa in /usr/local/lib/python3.6/dist-packages (0.6.3)\n",
      "Requirement already satisfied: numpy>=1.8.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.18.5)\n",
      "Requirement already satisfied: six>=1.3 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.15.0)\n",
      "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.16.0)\n",
      "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (4.4.2)\n",
      "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.4.1)\n",
      "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.22.2.post1)\n",
      "Requirement already satisfied: numba>=0.38.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.48.0)\n",
      "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (2.1.8)\n",
      "Requirement already satisfied: resampy>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.2.2)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (49.1.0)\n",
      "Requirement already satisfied: llvmlite<0.32.0,>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (0.31.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install librosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xDwZRcWEbetT",
    "outputId": "c8da55e4-b0be-4dbc-b9a3-a7505d1bb2e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1713\n"
     ]
    }
   ],
   "source": [
    "!ls -1 data/train/bed | wc -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fBuLhf0Wbr9c"
   },
   "outputs": [],
   "source": [
    "import librosa\n",
    "x, sr  = librosa.load('data/train/bed/58df33b5_nohash_0.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "colab_type": "code",
    "id": "bIZX4dMeb88p",
    "outputId": "046cdb0b-5a92-4cb7-d8fd-a0986998d90c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" >\n",
       "                    <source 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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython.display as ipd\n",
    "ipd.Audio(x, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "colab_type": "code",
    "id": "2kbCMI8ecZwr",
    "outputId": "532ebdb7-9330-4414-de79-0be2d9773bcf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7f000c9b9048>"
      ]
     },
     "execution_count": 69,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import librosa.display\n",
    "\n",
    "plt.figure(figsize=(14, 5))\n",
    "librosa.display.waveplot(x, sr=sr, alpha=0.8)  # also called pressure-time plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_6cFala9fuzk",
    "outputId": "e9e4009d-2de0-4810-8232-af9f50f4f6f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22050,)"
      ]
     },
     "execution_count": 71,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "colab_type": "code",
    "id": "m9SHCTSBfz6G",
    "outputId": "75e41b98-b4f5-468d-bcac-4bb019d55883"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f000c55e518>"
      ]
     },
     "execution_count": 81,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = librosa.stft(x)  # short-term fourier transform\n",
    "Xdb = librosa.amplitude_to_db(abs(X))\n",
    "plt.figure(figsize=(14, 5))\n",
    "librosa.display.specshow(Xdb, sr=sr, x_axis='time', y_axis='log')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66,
     "referenced_widgets": [
      "f4ffa73cf9914148ae75d6b1df7fe95b",
      "b7aedba8a9a646a3885b8deda2c916b6",
      "8a79881fca754e79a3728864c2a4de15",
      "4519f732abc5475d8ec9066fce21d715",
      "987f69271e8c48eca2911d8a2d5eb192",
      "a0a37d03865f4898a7ec61666d811655",
      "f45e610f11ac42a0976a1ce9169bfe45",
      "030577c0a5b649488397f6953ce58d13"
     ]
    },
    "colab_type": "code",
    "id": "I8DJacOjYDHd",
    "outputId": "1435cb11-8f9d-4e54-b1df-d9b80e596722"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4ffa73cf9914148ae75d6b1df7fe95b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1713.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "\n",
    "def vectorize_directory(dirpath, label=0):\n",
    "  features = []\n",
    "  labels = [0]\n",
    "  files = os.listdir(dirpath)\n",
    "  for filename in tqdm(files):\n",
    "    x, _ = librosa.load(\n",
    "        os.path.join(dirpath, filename)\n",
    "    )\n",
    "    features.append(x)\n",
    "  return features, [label] * len(features)\n",
    "\n",
    "features, labels = vectorize_directory('data/train/bed/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 115,
     "referenced_widgets": [
      "243b252104d74fa59af3cb5da3f6ba11",
      "dc34f249b1ac43289ca9714aedf2b189",
      "31c25c37f37347599f427bc8e83fe766",
      "fd373828ead4409c81b9f25666fb0c79",
      "f7edc939e47a42628d6d8e80b8e6c8af",
      "7d65e3c243b84cbdac5a7ded57b1203a",
      "2ba1f30d20f3469587dea357872d003a",
      "35f5fb5ea3994a9cbeff8fdf9d6ed3e0",
      "579961e517fd46ac80fa15e2193a70b8",
      "b063e1a51b54445690a161fbc6ebd362",
      "232bd7ad710044b5ba04d4b02400a4fa",
      "7a80b5d39ace474c9c9d2ef7868da57b",
      "6ddbcae870db4fc79202877ccf6c9aa8",
      "0c309170888746848201a23e57f9a068",
      "dfb44fcb7ab94e2abddb8b560d92167a",
      "c12e5fd3ac0440569ecc5e636e47c571"
     ]
    },
    "colab_type": "code",
    "id": "85zVb9x6cOuC",
    "outputId": "4ae3766b-5728-4dea-b03d-ffe2bc669cc2"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "243b252104d74fa59af3cb5da3f6ba11",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1731.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "579961e517fd46ac80fa15e2193a70b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1733.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "f, l = vectorize_directory('data/train/bird/', 1)\n",
    "features.extend(f)\n",
    "labels.extend(l)\n",
    "f, l = vectorize_directory('data/train/tree/', 2)\n",
    "features.extend(f)\n",
    "labels.extend(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QlyIyRgAfYYc"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump([features, labels], open('dataset.pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p3kZ7C_cRmoI"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "features, labels = pickle.load(open('dataset.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4WX8axQMa12_",
    "outputId": "1ff1b0ea-521b-4022-e120-39c4737e349d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5177"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "2xwKZcmOa3hM",
    "outputId": "d48670fe-cd54-4613-e8f9-09d7d8a8ed99"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00528411, -0.00746143, -0.00939442, ..., -0.00819206,\n",
       "       -0.00954113, -0.00630831], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 418
    },
    "colab_type": "code",
    "id": "WjmGK4o5yD19",
    "outputId": "ce870507-71fe-4c55-a59f-d215ee1c6c1a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.000e+00, 2.000e+00, 6.000e+00, 0.000e+00, 8.000e+00, 7.000e+00,\n",
       "        5.000e+00, 1.600e+01, 1.600e+01, 2.000e+01, 1.800e+01, 4.900e+01,\n",
       "        2.900e+01, 5.100e+01, 8.600e+01, 4.300e+01, 5.200e+01, 1.250e+02,\n",
       "        6.000e+00, 4.637e+03]),\n",
       " array([ 9409.  , 10041.05, 10673.1 , 11305.15, 11937.2 , 12569.25,\n",
       "        13201.3 , 13833.35, 14465.4 , 15097.45, 15729.5 , 16361.55,\n",
       "        16993.6 , 17625.65, 18257.7 , 18889.75, 19521.8 , 20153.85,\n",
       "        20785.9 , 21417.95, 22050.  ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist([f.shape[0] for f in features], bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Gyn1z_0FyVKA"
   },
   "outputs": [],
   "source": [
    "indexes = [i for i, f in enumerate(features) if f.shape[0] >= 22050]\n",
    "features = [f for i, f in enumerate(features) if i in indexes]\n",
    "labels = [f for i, f in enumerate(labels) if i in indexes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 414
    },
    "colab_type": "code",
    "id": "Eo_OHGq7zDH4",
    "outputId": "d791549f-e1e1-4662-988a-2006e1cedc5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0., 4526.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.]),\n",
       " array([22049.5 , 22049.55, 22049.6 , 22049.65, 22049.7 , 22049.75,\n",
       "        22049.8 , 22049.85, 22049.9 , 22049.95, 22050.  , 22050.05,\n",
       "        22050.1 , 22050.15, 22050.2 , 22050.25, 22050.3 , 22050.35,\n",
       "        22050.4 , 22050.45, 22050.5 ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([f.shape[0] for f in features], bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "46gSfE-fxWvq"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "features = np.concatenate([f.reshape(1, -1) for f in features], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Ee8oLjegxgdU",
    "outputId": "2b744aa1-e11e-499d-a9ac-bb65f9d997e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4526, 22050)"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BUo1jD51zRK4"
   },
   "outputs": [],
   "source": [
    "labels = np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NCa7dounSDhc"
   },
   "outputs": [],
   "source": [
    "classes = ['bed', 'bird', 'tree']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I0zxNtjdbS1Z"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "  features, labels, test_size=0.33, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "V5JJ_sWeSQoy",
    "outputId": "c0f4cdd8-5798-43d0-b4c2-97579386ba4f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3032, 22050)"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 459
    },
    "colab_type": "code",
    "id": "K1TLv5pqT-U7",
    "outputId": "aefdd20a-a3ed-4f2d-e6b2-85765d446755"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"conv1d_sound\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_46 (InputLayer)        [(None, 22050)]           0         \n",
      "_________________________________________________________________\n",
      "lambda_44 (Lambda)           (None, 22050)             0         \n",
      "_________________________________________________________________\n",
      "reshape_86 (Reshape)         (None, 22050, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_56 (Conv1D)           (None, 21951, 100)        10000     \n",
      "_________________________________________________________________\n",
      "batch_normalization_43 (Batc (None, 21951, 100)        400       \n",
      "_________________________________________________________________\n",
      "activation_43 (Activation)   (None, 21951, 100)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_29 (MaxPooling (None, 219, 100)          0         \n",
      "_________________________________________________________________\n",
      "reshape_87 (Reshape)         (None, 21900)             0         \n",
      "_________________________________________________________________\n",
      "dense_33 (Dense)             (None, 3)                 65703     \n",
      "=================================================================\n",
      "Total params: 76,103\n",
      "Trainable params: 75,903\n",
      "Non-trainable params: 200\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.models import Model\n",
    "import tensorflow.keras.backend as K\n",
    "\n",
    "\n",
    "def preprocess(x):\n",
    "    x = (x + 0.8) / 7.0\n",
    "    x = K.clip(x, -5, 5)\n",
    "    return x\n",
    "\n",
    "def relu6(x):\n",
    "    return K.relu(x, max_value=6)\n",
    "\n",
    "Preprocess = Lambda(preprocess)\n",
    "\n",
    "def conv_layer(x, num_filters=100, k=3, strides=2):\n",
    "    x = Conv1D(\n",
    "          num_filters,\n",
    "          (k),\n",
    "          padding='valid',\n",
    "          use_bias=False,\n",
    "          kernel_regularizer=l2(1e-6)\n",
    "        )(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(relu6)(x)\n",
    "    x = MaxPool1D(pool_size=num_filters, strides=None, padding='valid')(x)\n",
    "    return x\n",
    "\n",
    "def create_model(classes, nlayers=1, filters=100, k=100):\n",
    "    input_layer = Input(shape=[features.shape[1]])\n",
    "    x = Preprocess(input_layer)\n",
    "    x = Reshape([features.shape[1], 1])(x)\n",
    "    for _ in range(nlayers):\n",
    "        x = conv_layer(x, num_filters=filters, k=k)\n",
    "        x = Reshape([219 * filters])(x)\n",
    "        x = Dense(\n",
    "            units=len(classes), activation='softmax',\n",
    "            kernel_regularizer=l2(1e-2)\n",
    "        )(x)\n",
    "    model = Model(input_layer, x, name='conv1d_sound')\n",
    "    model.compile(\n",
    "        optimizer=keras.optimizers.Adam(lr=3e-4),\n",
    "        loss=keras.losses.SparseCategoricalCrossentropy(),\n",
    "        metrics=[keras.metrics.sparse_categorical_accuracy])\n",
    "    model.summary()\n",
    "    return model\n",
    "\n",
    "model = create_model(classes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "j8kn3-eCcvCL",
    "outputId": "f14a1712-8785-48d1-f8e1-dee13614c0cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "95/95 [==============================] - 18s 189ms/step - loss: 0.9745 - sparse_categorical_accuracy: 0.6052\n",
      "Epoch 2/30\n",
      "95/95 [==============================] - 18s 187ms/step - loss: 0.6555 - sparse_categorical_accuracy: 0.8001\n",
      "Epoch 3/30\n",
      "95/95 [==============================] - 18s 187ms/step - loss: 0.5567 - sparse_categorical_accuracy: 0.8318\n",
      "Epoch 4/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.5076 - sparse_categorical_accuracy: 0.8572\n",
      "Epoch 5/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.4726 - sparse_categorical_accuracy: 0.8724\n",
      "Epoch 6/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.4652 - sparse_categorical_accuracy: 0.8766\n",
      "Epoch 7/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.4396 - sparse_categorical_accuracy: 0.8839\n",
      "Epoch 8/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.4321 - sparse_categorical_accuracy: 0.8859\n",
      "Epoch 9/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.4181 - sparse_categorical_accuracy: 0.8978\n",
      "Epoch 10/30\n",
      "95/95 [==============================] - 18s 187ms/step - loss: 0.4112 - sparse_categorical_accuracy: 0.8902\n",
      "Epoch 11/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.4041 - sparse_categorical_accuracy: 0.8981\n",
      "Epoch 12/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3863 - sparse_categorical_accuracy: 0.9007\n",
      "Epoch 13/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3783 - sparse_categorical_accuracy: 0.9053\n",
      "Epoch 14/30\n",
      "95/95 [==============================] - 18s 187ms/step - loss: 0.3753 - sparse_categorical_accuracy: 0.9053\n",
      "Epoch 15/30\n",
      "95/95 [==============================] - 18s 187ms/step - loss: 0.3681 - sparse_categorical_accuracy: 0.9106\n",
      "Epoch 16/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3629 - sparse_categorical_accuracy: 0.9096\n",
      "Epoch 17/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.9050\n",
      "Epoch 18/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3636 - sparse_categorical_accuracy: 0.9077\n",
      "Epoch 19/30\n",
      "95/95 [==============================] - 18s 187ms/step - loss: 0.3584 - sparse_categorical_accuracy: 0.9080\n",
      "Epoch 20/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3386 - sparse_categorical_accuracy: 0.9192\n",
      "Epoch 21/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3475 - sparse_categorical_accuracy: 0.9109\n",
      "Epoch 22/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3391 - sparse_categorical_accuracy: 0.9212\n",
      "Epoch 23/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3306 - sparse_categorical_accuracy: 0.9192\n",
      "Epoch 24/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3418 - sparse_categorical_accuracy: 0.9116\n",
      "Epoch 25/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3277 - sparse_categorical_accuracy: 0.9271\n",
      "Epoch 26/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3228 - sparse_categorical_accuracy: 0.9258\n",
      "Epoch 27/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3113 - sparse_categorical_accuracy: 0.9314\n",
      "Epoch 28/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3133 - sparse_categorical_accuracy: 0.9314\n",
      "Epoch 29/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3097 - sparse_categorical_accuracy: 0.9268\n",
      "Epoch 30/30\n",
      "95/95 [==============================] - 18s 186ms/step - loss: 0.3163 - sparse_categorical_accuracy: 0.9238\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f4ad42ab588>"
      ]
     },
     "execution_count": 94,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "gGZiq4iCc0YU",
    "outputId": "0bd9117f-136e-424d-b165-ab593eccf92b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.805890\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "\n",
    "predicted = model.predict(X_test)\n",
    "print('accuracy: {:.3f}'.format(\n",
    "    sklearn.metrics.accuracy_score(y_test, predicted.argmax(axis=1))\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "colab_type": "code",
    "id": "nilTg6OuYkzL",
    "outputId": "b24806e3-d5e8-42dd-ef6b-c4d0d72d8a65"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([479.,   0.,   0.,   0.,   0., 541.,   0.,   0.,   0., 474.]),\n",
       " array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 106,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(predicted.argmax(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 333
    },
    "colab_type": "code",
    "id": "iaVErX07dHrE",
    "outputId": "f1403a3d-4ff4-48eb-8a3f-a17a793a1704"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1484.,    0.,    0.,    0.,    0., 1521.,    0.,    0.,    0.,\n",
       "        1521.]),\n",
       " array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 103,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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